1. Field of the Disclosure
The present disclosure relates generally to an apparatus and method for estimating a device's location in an interior location/environment, and more particularly, to an apparatus and method which increases the accuracy of location estimation in a wireless communication system.
2. Description of the Related Art
Recently, location based services (LBSs) have been actively studied for use in mobile terminals, e.g., smart phones. The typical scheme for estimating a location of a mobile terminal uses a global navigational satellite system (GNSS), such as the global positioning system (GPS). However, the received signal strength of a GPS satellite signal can be very weak, and the mobile terminal may not be able to detect its own location accurately.
In environments where the GPS satellite signal is weak and/or scrambled, such as in an interior environment, schemes for measuring a location of a mobile terminal using a short-range wireless communication systems such as radio frequency identification (RFID), BLUETOOTH™, wireless local area network (WLAN), and the like have been actively studied.
Among the short-range communication schemes, WLAN has an extensively built infrastructure and support base, and is one of the most widely used wireless systems in interior locations. Accordingly, WLAN could be a scheme for estimating a location of a mobile terminal in an interior environment.
Interior location estimating schemes using WLAN may be classified as either non-parametric or parametric, as will be described below.
The non-parametric approach scheme does not use parameters which may specify a system. A typical non-parametric approach is the fingerprint scheme. In the fingerprint scheme, a mobile terminal previously measures and stores a received signal strength indicator (RSSI) or a round trip time (RTT) value at each of a plurality of points of a signal received from a wireless access point (AP). The mobile terminal estimates a grid point which is most similar to strength of a received signal which is actually received as a location of the mobile terminal. The plurality of points may be generated by dividing an interior map on a grid basis or according to a preset rule.
The parametric approach scheme uses parameters to estimate the location of the mobile terminal. A typical parametric approach is to use a path loss model.
Both the parametric and non-parametric interior location estimating schemes require a training phase.
The training phase denotes a phase whereby a mobile terminal divides an area at which the mobile terminal intends to provide an LBS on a grid basis, and stores an average measurement value of an RSSI or RTT of a received signal received from an AP adjacent to each of grid points at each of the grid points at a database.
The (non-parametric) fingerprint interior location estimating scheme has a higher accuracy than any parametric approach scheme, so the fingerprint interior locating estimation scheme is used more than any based on the parametric approach.
However, in the fingerprint-based interior location estimating scheme, a server needs to generate a database for all possible grid points on a map, so the training phase requires much manpower and time, and the server needs to transmit information on received signals received from all APs adjacent to each grid point at each grid point to a mobile terminal, so the amount of data which is transmitted from the server to the mobile terminal is quite large.
By contrast, parametric interior location estimating schemes do not have the same problems with extensive data resource usage that can occur using the fingerprint-based non-parametric scheme. However, as stated above, parametric interior location estimating schemes have a lower accuracy than the fingerprint-based non-parametric scheme, so parametric interior location estimating schemes are not as actively studied.